Low-Complexity Hybrid Precoding for Cell-Free Massive MU-MIMO ISAC Systems

2026-06-14

AI Summary

This work addresses the high fronthaul overhead and computational complexity arising from centralized coordination in cell-free massive MU-MIMO integrated sensing and communication (ISAC) systems by proposing a partially connected hybrid precoding architecture. By compressing the high-dimensional channel into a low-dimensional equivalent representation, the approach significantly reduces fronthaul load and baseband complexity while jointly optimizing digital and analog precoders to maximize ergodic sum rate under multi-target localization accuracy constraints. An innovative alternating optimization framework is introduced to transform the non-convex positioning error constraints into tractable convex forms. Furthermore, analog precoding is performed over a constant-modulus manifold, combined with a multi-branch rate-splitting MMSE-Tomlinson-Harashima precoder, thereby avoiding full-matrix recomputation in dynamic user scenarios. The proposed scheme achieves an 87.02% reduction in computational complexity compared to conventional methods while maintaining both communication and sensing performance.

Abstract

Integrated sensing and communication (ISAC) in cell-free (CF) massive multi-user multiple-input multiple-output (MU-MIMO) system is a promising architecture for high-rate communications and high-accuracy multi-target sensing. However, centralized coordination among distributed access points (APs) incurs substantial fronthaul overhead and computation complexity. This paper proposes a low-complexity hybrid precoding framework for CF massive MU-MIMO ISAC systems with partially-connected architectures at the APs. By applying hybrid architecture at the APs, the proposed framework converts the original high-dimensional channel information into a low-dimensional effective channel, enabling digital precoding over the compressed channel domain and thereby substantially reducing both fronthaul overhead and baseband computational complexity. We formulate the joint hybrid precoding design as an ergodic sum-rate (ESR) maximization problem with position error bound (PEB) constraints to ensure multi-target sensing accuracy. An efficient alternating optimization (AO)-based solver is then developed, where the PEB constraint is reformulated into tractable convex constraints, while the digital-domain optimization is carried out over the reduced-dimensional effective channel and the analog precoding is refined on the constant-modulus manifold. For dynamic user topology, we further propose multi-branch (MB) rate-splitting (RS) minimum mean-square-error Tomlinson-Harashima precoding (MMSE-THP) update algorithm that combines multi-branch ordering with recursive MMSE-THP matrix updates, enabling common and private digital precodings to be refreshed without repeated full matrix recomputation. Simulation results demonstrate that the proposed scheme achieves high ESR and accurate multi-target sensing while reducing computational complexity by 87.02\% compared with conventional baselines.

Problem

• Cell-Free Massive MU-MIMO
• Integrated Sensing and Communication (ISAC)
• Fronthaul Overhead
• Computational Complexity
• Hybrid Precoding

Innovation

• hybrid precoding
• cell-free massive MU-MIMO
• integrated sensing and communication (ISAC)
• low-complexity optimization
• rate-splitting MMSE-THP